부산대학교 도서관

In this paper we propose a method combining electrohysterography (EHG) and heart rate (HR) data to detect labour. Labour detection may be helpful in providing just in time care and avoiding unnecessary antenatal visits. Given specific changes in physiological data such as EHG and HR highlighted from previous literature in correspondence of uterine contractions, we sought to create a model able to classify between labour and non-labour recordings based on EHG and maternal HR data. In particular, we collected 37 recordings (19 labour and 18 non-labour) from pregnant women at different stages of pregnancy using a wearable sensor designed to be attached to the abdomen using an adhesive patch. We extracted time and frequency domain features from EHG and HR data, as stronger, sinusoidal patterns arise on both data streams in correspondence with uterine contractions during labour. Features were used as input to a random forests classifier, trained to recognize labour and non-labour recordings. The accuracy of the proposed model in classifying labour and non-labour recordings was evaluated using leave one out cross validation. We analyzed results including as predictors; gestational age (GA) only, as reference lower bound (68% accuracy), EHG features only (71% accuracy), HR features only (71% accuracy) and combined EHG and HR data, resulting in 82% accuracy. Inclusion of GA as additional predictor further increased detection accuracy to 79%, 82% and 87% for EHG, HR and combined EHG and HR respectively. Our labour detection model demonstrated a high accuracy in classifying labour and non-labour recordings using EHG and HR data collected using a single wearable device.